



'''simple docstring'''



import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType
class 						lowerCamelCase_				:







						def __init__(	self :   List[Any]					,     lowerCAmelCase__ :   List[Any]=2					,     lowerCAmelCase__ :   Any=3					,     lowerCAmelCase__ :   Optional[Any]=64					,     lowerCAmelCase__ :   str=None					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      np.random.default_rng(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Any         =      length
													SCREAMING_SNAKE_CASE       :   List[Any]         =      rng.normal(size=(length,)					).astype(np.floataa					)
													SCREAMING_SNAKE_CASE       :   Dict         =      a * self.x + b + rng.normal(scale=0.1					,     size=(length,)					).astype(np.floataa					)







						def __len__(	self :   Optional[Any]					):

													"""simple docstring"""
													return self.length







						def __getitem__(	self :   Tuple					,     lowerCAmelCase__ :   int					):

													"""simple docstring"""
													return {"x": self.x[i], "y": self.y[i]}


class 						lowerCamelCase_				(       torch.nn.Module      ):







						def __init__(	self :   Union[str, Any]					,     lowerCAmelCase__ :   Union[str, Any]=0					,     lowerCAmelCase__ :   List[str]=0					,     lowerCAmelCase__ :   Optional[Any]=False					):

													"""simple docstring"""
													super().__init__()
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      torch.nn.Parameter(torch.tensor([2, 3]					).float()					)
													SCREAMING_SNAKE_CASE       :   int         =      torch.nn.Parameter(torch.tensor([2, 3]					).float()					)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      True







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   Union[str, Any]=None					):

													"""simple docstring"""
													if self.first_batch:
																				print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""					)
																				SCREAMING_SNAKE_CASE       :   int         =      False
													return x * self.a[0] + self.b[0]


class 						lowerCamelCase_				(       torch.nn.Module      ):







						def __init__(	self :   Tuple					,     lowerCAmelCase__ :   List[Any]=0					,     lowerCAmelCase__ :   Optional[int]=0					,     lowerCAmelCase__ :   Optional[Any]=False					):

													"""simple docstring"""
													super().__init__()
													SCREAMING_SNAKE_CASE       :   Any         =      torch.nn.Parameter(torch.tensor(lowerCAmelCase__					).float()					)
													SCREAMING_SNAKE_CASE       :   int         =      torch.nn.Parameter(torch.tensor(lowerCAmelCase__					).float()					)
													SCREAMING_SNAKE_CASE       :   int         =      True







						def        __lowercase     (	self :   str					,     lowerCAmelCase__ :   str=None					):

													"""simple docstring"""
													if self.first_batch:
																				print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""					)
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      False
													return x * self.a + self.b


def        UpperCAmelCase (    A	:					Optional[int]       ,		A	:					int = 16					):
							from datasets import load_dataset
							from transformers import AutoTokenizer

							SCREAMING_SNAKE_CASE       :   Optional[int]         =      AutoTokenizer.from_pretrained('''bert-base-cased'''					)
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      load_dataset('''csv'''       ,		data_files=A					)
							SCREAMING_SNAKE_CASE       :   Tuple         =      datasets['''train'''].unique('''label'''					)

							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      {v: i for i, v in enumerate(A					)}

							def tokenize_function(A	:					int					):
														# max_length=None => use the model max length (it's actually the default)
														SCREAMING_SNAKE_CASE       :   List[str]         =      tokenizer(
														    examples['''sentence1''']       ,		examples['''sentence2''']       ,		truncation=A       ,		max_length=A       ,		padding='''max_length'''					)
														if "label" in examples:
																					SCREAMING_SNAKE_CASE       :   Tuple         =      [label_to_id[l] for l in examples['''label''']]
														return outputs

							# Apply the method we just defined to all the examples in all the splits of the dataset
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      datasets.map(
							    A       ,		batched=A       ,		remove_columns=['''sentence1''', '''sentence2''', '''label''']       ,		)

							def collate_fn(A	:					List[str]					):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														if accelerator.distributed_type == DistributedType.TPU:
																					return tokenizer.pad(A       ,		padding='''max_length'''       ,		max_length=128       ,		return_tensors='''pt'''					)
														return tokenizer.pad(A       ,		padding='''longest'''       ,		return_tensors='''pt'''					)

							# Instantiate dataloaders.
							SCREAMING_SNAKE_CASE       :   Dict         =      DataLoader(tokenized_datasets['''train''']       ,		shuffle=A       ,		collate_fn=A       ,		batch_size=2					)
							SCREAMING_SNAKE_CASE       :   Dict         =      DataLoader(tokenized_datasets['''validation''']       ,		shuffle=A       ,		collate_fn=A       ,		batch_size=1					)

							return train_dataloader, eval_dataloader









'''simple docstring'''



def        UpperCAmelCase (    A	:					int       ,		A	:					int					):
							return x if y == 0 else greatest_common_divisor(A       ,		x % y					)
def        UpperCAmelCase (    A	:					int       ,		A	:					int					):
							return (x * y) // greatest_common_divisor(A       ,		A					)
def        UpperCAmelCase (    A	:					int = 20					):
							SCREAMING_SNAKE_CASE       :   Tuple         =      1
							for i in range(1       ,		n + 1					):
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      lcm(A       ,		A					)
							return g


if __name__ == "__main__":
							print(f'{solution() = }')




